Surface defect characterization and depth identification of CFRP material by laser line scanning

The detection of defects on the surface of carbon fiber reinforced polymer has increasingly become the focus of modern NDT research. In this paper, the shape characterization and depth identification of surface defects of CFRP materials are investigated by establishing reflective and transmissive line laser infrared thermography nondestructive inspection systems. First, we verified the feasibility of the work by simulation. Then, the temperature variation of surface defects was analyzed by two experimental schemes, reflective mode and transmissive mode. To characterize the shape of the defects, we deduced the size of the detect from the scan of the line laser. The results show that the characterization accuracy of defect size is different for different scanning speeds, and finally the characterization error can be controlled within 2.2%. In order to achieve the defect depth classification, we used the grey wolf optimization algorithm to optimize the hyper-parameters in the support vector machine, which can finally achieve 97% depth classification accuracy in 0.56s.